论文部分内容阅读
在一个信道上传送多个用户信号，可以大大提高信道的容量．本文讨论了非理想信道多用户数字信号的盲分离问题．利用天线阵，接收信号可以看作是由Ｎ个独立信号源激励的线性传输混合系统的输出；由于信道存在码间干扰，混合矩阵的元素不是常数，而是一个线性子系统．针对这一情况，本文提出了一个新的盲分离器结构，首先将接收信号进行盲分离，然后利用基于ＡＲ模型的盲均衡器消除每一路输出信号的码间干扰，从而实现多用户信号的分离．文中给出了盲分离器的神经网络学习算法，讨论了其稳定性及收敛性．模拟结果显示分离效果是令人满意的．
Transmitting multiple user signals on one channel can greatly increase the channel capacity. This paper discusses the problem of blind separation of non-ideal channel multi-user digital signals. Using the antenna array, the received signal can be viewed as the output of a linearly transmitted hybrid system excited by N independent sources. Because of the intersymbol interference in the channel, the elements of the mixed matrix are not constants but a linear subsystem. In view of this situation, this paper presents a new structure of blind separators. Firstly, the received signals are blindly separated. Then the blind equalizer based on AR model is used to eliminate the intersymbol interference of each output signal, so as to realize the separation of multiuser signals . In this paper, a neural network learning algorithm for blind separators is given, and its stability and convergence are discussed. The simulation results show that the separation effect is satisfactory.